Deletion of Flagellin's hypervariable region abrogates antibody-mediated neutralization and systemic activation of TLR5-dependent immunity

TLRs trigger immunity by detecting microbe-associated molecular patterns (MAMPs). Flagellin is a unique MAMP because it harbors 1) an antigenic hypervariable region and 2) a conserved domain involved in TLR5-dependent systemic and mucosal proinflammatory and adjuvant activities. In this study, the contribution of the flagellin domains in TLR5 activation was investigated. We showed that TLR5 signaling can be neutralized in vivo by flagellin-specific Abs, which target the conserved domain. However, deletions of flagellin's hypervariable region abrogated the protein's intrinsic ability to trigger the production of neutralizing Abs. The fact that MAMP-specific Abs block TLR-mediated responses shows that this type of neutralization is a novel mechanism for down-regulating innate immunity. The stimulation of mucosal innate immunity and adjuvancy to foreign Ag was not altered by the hypervariable domain deletions. In contrast, this domain is essential to trigger systemic innate immunity, suggesting that there are distinct mechanisms for TLR5 activation in systemic and mucosal compartments. In summary, specific MAMP determinants control the production of neutralizing Abs and the compartmentalization of innate responses.Laboratorio de Investigaciones del Sistema Inmun

Deletion of Flagellin's hypervariable region abrogates antibody-mediated neutralization and systemic activation of TLR5-dependent immunity

TLRs trigger immunity by detecting microbe-associated molecular patterns (MAMPs). Flagellin is a unique MAMP because it harbors 1) an antigenic hypervariable region and 2) a conserved domain involved in TLR5-dependent systemic and mucosal proinflammatory and adjuvant activities. In this study, the contribution of the flagellin domains in TLR5 activation was investigated. We showed that TLR5 signaling can be neutralized in vivo by flagellin-specific Abs, which target the conserved domain. However, deletions of flagellin's hypervariable region abrogated the protein's intrinsic ability to trigger the production of neutralizing Abs. The fact that MAMP-specific Abs block TLR-mediated responses shows that this type of neutralization is a novel mechanism for down-regulating innate immunity. The stimulation of mucosal innate immunity and adjuvancy to foreign Ag was not altered by the hypervariable domain deletions. In contrast, this domain is essential to trigger systemic innate immunity, suggesting that there are distinct mechanisms for TLR5 activation in systemic and mucosal compartments. In summary, specific MAMP determinants control the production of neutralizing Abs and the compartmentalization of innate responses.Laboratorio de Investigaciones del Sistema Inmun

Mucosal interplay among commensal and pathogenic bacteria : Lessons from flagellin and Toll-like receptor 5

Toll-like receptors (TLR) detect pathogen-associated molecular patterns (PAMP) and play a crucial role in triggering immunity. Due to their large surfaces in direct contact with the environment, mucosal tissues are the major sites of PAMP-TLR signalling. How innate and adaptive immunity are triggered through flagellin-TLR5 interaction is the main focus of the review. In view of recent reports on genetic polymorphism, we will summarize the impact of TLR5 on the susceptibility to mucosal infections and on various immuno-pathologies. Finally, the contribution of TLRs in the induction and maintenance of mucosal homeostasis and commensal discrimination is discussed.Facultad de Ciencias Exacta

Synthetic Toll Like Receptor-4 (TLR-4) Agonist Peptides as a Novel Class of Adjuvants

Background: Adjuvants serve as catalysts of the innate immune response by initiating a localized site of inflammation that is mitigated by the interactions between antigens and toll like receptor (TLR) proteins. Currently, the majority of vaccines are formulated with aluminum based adjuvants, which are associated with various side effects. In an effort to develop a new class of adjuvants, agonists of TLR proteins, such as bacterial products, would be natural candidates. Lipopolysaccharide (LPS), a major structural component of gram negative bacteria cell walls, induces the systemic inflammation observed in septic shock by interacting with TLR-4. The use of synthetic peptides of LPS or TLR-4 agonists, which mimic the interaction between TLR-4 and LPS, can potentially regulate cellular signal transduction pathways such that a localized inflammatory response is achieved similar to that generated by adjuvants. Methodology/Principal Findings: We report the identification and activity of several peptides isolated using phage display combinatorial peptide technology, which functionally mimicked LPS. The activity of the LPS-TLR-4 interaction was assessed by NF-kB nuclear translocation analyses in HEK-BLUE TM-4 cells, a cell culture model that expresses only TLR-4, and the murine macrophage cell line, RAW264.7. Furthermore, the LPS peptide mimics were capable of inducing inflammatory cytokine secretion from RAW264.7 cells. Lastly, ELISA analysis of serum from vaccinated BALB/c mice revealed that the LPS peptide mimics act as a functional adjuvant

Intranasal Immunization with Influenza VLPs Incorporating Membrane-Anchored Flagellin Induces Strong Heterosubtypic Protection

We demonstrated previously that the incorporation of a membrane-anchored form of flagellin into influenza virus-like particles (VLPs) improved the immunogenicity of VLPs significantly, inducing partially protective heterosubtypic immunity by intramuscular immunization. Because the efficacy of mucosal vaccination is highly dependent on an adjuvant, and is particularly effective for preventing mucosal infections such as influenza, we determined whether the membrane-anchored flagellin is an efficient adjuvant for VLP vaccines by a mucosal immunization route. We compared the adjuvant effect of membrane-anchored and soluble flagellins for immunization with influenza A/PR8 (H1N1) VLPs by the intranasal route in a mouse model. The results demonstrate that membrane-anchored flagellin is an effective adjuvant for intranasal (IN) immunization, inducing enhanced systemic and mucosal antibody responses. High cellular responses were also observed as shown by cytokine production in splenocyte cultures when stimulated with viral antigens. All mice immunized with flagellin-containing VLPs survived challenge with a high lethal dose of homologous virus as well as a high dose heterosubtypic virus challenge (40 LD50 of A/Philippines/82, H3N2). In contrast, no protection was observed with a standard HA/M1 VLP group upon heterosubtypic challenge. Soluble flagellin exhibited a moderate adjuvant effect when co-administered with VLPs by the mucosal route, as indicated by enhanced systemic and mucosal responses and partial heterosubtypic protection. The membrane-anchored form of flagellin incorporated together with antigen into influenza VLPs is effective as an adjuvant by the mucosal route and unlike standard VLPs, immunization with such chimeric VLPs elicits protective immunity to challenge with a distantly related influenza A virus

Application of built-in adjuvants for epitope-based vaccines

Several studies have shown that epitope vaccines exhibit substantial advantages over conventional vaccines. However, epitope vaccines are associated with limited immunity, which can be overcome by conjugating antigenic epitopes with built-in adjuvants (e.g., some carrier proteins or new biomaterials) with special properties, including immunologic specificity, good biosecurity and biocompatibility, and the ability to vastly improve the immune response of epitope vaccines. When designing epitope vaccines, the following types of built-in adjuvants are typically considered: (1) pattern recognition receptor ligands (i.e., toll-like receptors); (2) virus-like particle carrier platforms; (3) bacterial toxin proteins; and (4) novel potential delivery systems (e.g., self-assembled peptide nanoparticles, lipid core peptides, and polymeric or inorganic nanoparticles). This review primarily discusses the current and prospective applications of these built-in adjuvants (i.e., biological carriers) to provide some references for the future design of epitope-based vaccines

Modèles structurels flous et propagation de contraintes pour la segmentation et la reconnaissance d'objets dans les images: Application aux structures normales et pathologiques du cerveau en IRM

Le cerveau présente une structure complexe. La segmentation et la reconnaissance automatique de ses sous-structures dans des IRM cérébrales est délicate et nécessite donc l'utilisation d'un modèle de l'anatomie. L'utilisation d'atlas iconiques est efficace pour traiter les données de sujets sains mais son adaptation au traitement de cas pathologiques reste problématique. Dans cette thèse nous utilisons un modèle symbolique de l'anatomie proche des descriptions linguistiques qui comprend les principales structures cérébrales. L'agencement spatial de ces structures y est représenté sous forme de relations spatiales et leur apparence est caractérisée par des relations sur leur contraste. Réaliser la reconnaissance grâce à ce modèle structurel consiste à obtenir pour chaque structure une région de l'image vérifiant les relations et caractéristiques portées par le modèle. Nous formulons ce problème comme un réseau de contraintes dont les variables sont les régions recherchées représentées sous forme d'ensembles flous. Les contraintes sont déduites du modèle en tirant parti de modélisations floues. Une contribution nouvelle porte sur la contrainte de connexité et la proposition de définitions et algorithmes adaptés au cas flou présentant de bonnes propriétés. Nous mettons alors en œuvre un algorithme de propagation de contraintes qui itérativement réduit l'espace de solutions. Enfin nous obtenons un résultat pour certaines structures d'intérêt par l'extraction d'une surface minimale relativement aux résultats de l'algorithme de propagation. Nous appliquons cette approche aux structures internes du cerveau chez des sujets sains. Finalement nous étendons ce processus au traitement de données de patients présentant une tumeur. Le modèle générique ne correspondant plus aux données à reconnaître, nous proposons un algorithme de propagation recherchant à la fois le modèle spécifique au patient et les structures anatomiques

Modèles structurels flous et propagation de contraintes pour la segmentation et la reconnaissance d'objets dans les images (application aux structures normales et pathologiques du cerveau en IRM)

Le cerveau présente une structure complexe. La segmentation et la reconnaissance automatique de ses sous-structures dans des IRM cérébrales est délicate et nécessite donc l'utilisation d'un modèle de l'anatomie. L'utilisation d'atlas iconiques est efficace pour traiter les données de sujets sains mais son adaptation au traitement de cas pathologiques reste problématique. Dans cette thèse nous utilisons un modèle symbolique de l'anatomie proche des descriptions linguistiques qui comprend les principales structures cérébrales. L'agencement spatial de ces structures y est représenté sous forme de relations spatiales et leur apparence est caractérisée par des relations sur leur contraste. Réaliser la reconnaissance grâce à ce modèle structurel consiste à obtenir pour chaque structure une région de l'image vérifiant les relations et caractéristiques portées par le modèle. Nous formulons ce problème comme un réseau de contraintes dont les variables sont les régions recherchées représentées sous forme d'ensembles flous. Les contraintes sont déduites du modèle en tirant parti de modélisations floues. Une contribution nouvelle porte sur la contrainte de connexité et la proposition de définitions et algorithmes adaptés au cas flou présentant de bonnes propriétés. Nous mettons alors en œuvre un algorithme de propagation de contraintes qui itérativement réduit l'espace de solutions. Enfin nous obtenons un résultat pour certaines structures d'intérêt par l'extraction d'une surface minimale relativement aux résultats de l'algorithme de propagation. Nous appliquons cette approche aux structures internes du cerveau chez des sujets sains. Finalement nous étendons ce processus au traitement de données de patients présentant une tumeur. Le modèle générique ne correspondant plus aux données à reconnaître, nous proposons un algorithme de propagation recherchant à la fois le modèle spécifique au patient et les structures anatomiques.The anatomy of brain is complex. Therefore the fully automatic segmentation and recognition of its relevant subparts in brain MRI is a challenging task. It is usually done using a model of anatomy. In this thesis we use a symbolic model of anatomy, close to linguistic descriptions. It includes the main brain structures and some of their properties.Their spatial layout is encoded as spatial relations and their appearance is represented as relations on their contrast. We use this structural model to perform the recognition : we have to obtain for each anatomical structure a region of the image that fulfils all relations and characteristics of the model. We formulate this problem as a constraint network whose variables are the sought regions represented as fuzzy sets. The constraints are derived from the model using fuzzy modeling. In particular to obtain the connectivity constraint, we propose a new definition (and the associated algorithms) for the connectivity of fuzzy sets.Then we implement a constraint propagation algorithm which iteratively reduces the solution space. Once the solution space has been reduced, we obtain a final result for some structures. We extract a minimal surface with respect to the outputs of the propagation algorithm. We apply this approach to brain internal structures of healthy subjects. Finally we propose an extension to handle cases that present brain tumors. The generic model of anatomy does not fit anymore the data to be recognized. Therefore we propose a propagation algorithm that searches simultaneously for the specific model of the patient for the anatomical structures.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Propagation de contraintes pour la segmentation et la reconnaissance de structures anatomiques à partir d'un modèle structurel

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